Trust is the foundation for learning, and we must not allow ignorance of this new technologies, like Generative AI, to disrupt the relationship between students and educators. As a first step, we need to actively engage with AI tools to better understand how they can help us in our work.

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential (...) gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

There is a history of thinking that photographs provide a special kind of access to the objects depicted in them, beyond the access that would be provided by a painting or drawing. What is included in the photograph does not depend on the photographer’s beliefs about what is in front of the camera. This feature leads Kendall Walton to argue that photographs literally allow us to see the objects which appear in them. Current generative algorithms produce images in response (...) to users’ text prompts. Depending on the parameters, the output can resemble specific people or things which are named in the prompt. This resemblance does not depend on the user’s beliefs, so generated images are in this sense like photographs. Given this parallel, how should we think about AI-generated images? (shrink)

This paper critically examines Japan's approach toward the adoption of Generative AI such as ChatGPT in education via studying media discourse and guidelines at both the national as well as local levels. It highlights the lack of consideration for socio-cultural characteristics inherent in the Japanese educational systems, such as the notion of self, teachers’ work ethics, community-centric activities for the successful adoption of the technology. We reveal ChatGPT’s infusion is likely to further accelerate the shift away from traditional notion (...) of self, which has been ongoing during past educational reforms, towards active and self-directed learning. The current discourse on Generative AI’s infusion in education is driven by neoliberal agendas aimed at increasing employability and individualism. Moreover, the introduction of ChatGPT in the context of Japan's educator culture offers potential solutions to address issues of overwork but also necessitates considerations regarding increased responsibilities for teachers and the preservation of their central role in student education. Finally, the irreplicable social growth fostered by community-centric activities in the Japanese educational system highlights the limitations of ChatGPT in replicating dynamic social interactions, emphasizing the importance of holistic approaches to education beyond technological integration. (shrink)

In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the (...) limited fine-grained control users have over its outputs, which sets it apart from traditional artistic tools. We propose that creating images with Midjourney parallels a gardener's interaction with natura naturans—nature's autonomous generative processes. Similarly, Midjourney acts as machina naturans, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process. This approach presents Midjourney as an artistic medium shaped by its inherent unpredictability. (shrink)

The recent wave of generative AI (GAI) systems like Stable Diffusion that can produce images from human prompts raises controversial issues about creatorship, originality, creativity and copyright. This paper focuses on creatorship: who creates and should be credited with the outputs made with the help of GAI? Existing views on creatorship are mixed: some insist that GAI systems are mere tools, and human prompters are creators proper; others are more open to acknowledging more significant roles for GAI, but most (...) conceive of creatorship in an all-or-nothing fashion. We develop a novel view, called CCC (collective-centered creation), that improves on these existing positions. On CCC, GAI outputs are created by collectives in the first instance. Claims to creatorship come in degrees and depend on the nature and significance of individual contributions made by the various agents and entities involved, including users, GAI systems, developers, producers of training data and others. Importantly, CCC maintains that GAI systems can sometimes be part of a co-creating collective. We detail how CCC can advance existing debates and resolve controversies around creatorship involving GAI. (shrink)

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research (...) on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia. (shrink)

The increasing use of Generative AI raises many ethical, philosophical, and legal issues. A key issue here is uncertainties about how different degrees of Generative AI assistance in the production of text impacts assessments of the human authorship of that text. To explore this issue, we developed an experimental mixed methods survey study (N = 602) asking participants to reflect on a scenario of a human author receiving assistance to write a short novel as part of a 3 (...) (high, medium, or low degrees of assistance) X 2 (human or AI assistant) factorial design. We found that, for a human author, the degree of assistance they receive matters for our assessments of their level of authorship, creatorship, and responsibility, but not what or who rendered that assistance, although it was more important to disclose human rather than AI assistance. However, in our assessments of the assisting agent, human assistants were viewed as warranting higher rates of authorship, creatorship, and responsibility, compared to AI assistants rendering the same level of support. These results help us to better understand emerging norms around collaborative human-AI generated text, with implications for other types of collaborative content creation. (shrink)

The research evaluates generative AI’s capabilities through a multi-phase framework, addressing how data synthesis, language models, and predictive algorithms contribute to sector-specific applications. In banking, the model assesses the impact of AI-driven chatbot interactions, credit risk assessment, and personalized financial services on customer experience and bank performance. Healthcare applications are explored through image synthesis for diagnostics, predictive modeling in patient care, and drug discovery simulations. The experimental setup is rigorously tested across metrics such as response accuracy, cost-effectiveness, and data (...) privacy to determine the benefits and potential risks associated with generative AI in these fields. (shrink)

Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.

In January 2024, non-consensual deepfakes came to public attention with the spread of AI generated sexually abusive images of Taylor Swift. Although this brought new found energy to the debate on what some call non-consensual synthetic intimate images (i.e. images that use technology such as AI or photoshop to make sexual images of a person without their consent), female celebrities like Swift have had deepfakes like these made of them for years. In 2017, a Reddit user named “deepfakes” posted several (...) videos in which he had used opensource machine learning tools to swap the faces of female celebrities on to the faces of female porn actors, displaying what appeared to be live video footage of the celebrity engaging in sex acts she never engaged in. Since that time, deepfake technology has advanced astronomically. What once were choppy sexualized videos are now nearly flawless videos that can be difficult to distinguish from a real video. According to recent research on deepfakes by Sensity AI, this technology has been used primarily on women to create sexual videos. These women’s sexual autonomy has been co-opted for the purposed of gratifying men’s sexual pleasure, but have also been used in campaigns to delegitimize and humiliate female journalists and politicians. In Canada, civil and criminal legislation has addressed the non-consensual distribution of intimate images, but in only a few provinces – British Columbia, New Brunswick, Prince Edward Island and Saskatchewan – does this offence include altered images that could include deepfakes. This paper explores the evolution of synthetic technology such as AI image generators and Canada’s legal responses to the non-consensual sharing of intimate images. (shrink)

Results indicate substantial improvements in efficiency, accuracy, and personalized care, but also highlight the challenges of data privacy, ethical considerations, and system scalability. By providing a structured analysis, this research contributes insights into optimizing generative AI deployments for both banking and healthcare, ensuring a balance between innovation and risk management. The study concludes with recommendations for future research directions, including advanced model training, ethical guidelines, and enhanced privacy measures. These insights aim to inform practitioners on the benefits of (...) class='Hi'>generative AI, ensuring sustainable integration into banking and healthcare ecosystems. (shrink)

“Throw away anything that sounds too complicated. Only keep what is simple to grasp...If the information appears fuzzy and causes the brain to implode after two sentences, toss it away and stop listening. Doing so will make the news as orderly and simple to understand as the truth.” - In “GHG emissions,” The Kingfisher Story Collection, (Vuong 2022a).

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research (...) on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia. (shrink)

The recent remarkable success of generative AI models to create text and images has already started altering our perspective of intelligence and the “uniqueness” of humanity in this world. Simultaneously, arguments on why AI will never exceed human intelligence are ever-present as seen in Landgrebe and Smith (2022). To address whether machines may rule the world after all, this paper zooms in on one of the aspects of intelligence Landgrebe and Smith (2022) neglected to consider: creativity. Using Rhodes four (...) Ps of creativity as a starting point, this paper evaluates the creative ability in visual generative AI models with respect to the state of the art in creativity theory. The most part of the reflective evaluation is performed through a case study in generating illustrations of dogs using the generative AI tool Midjourney. (shrink)

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues (...) that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other things, Habermas's inquiries (...) into the analogy between human minds and computers, and into the status of atypical participants in the linguistic community such as genetically modified subjects and animals. Major conclusions are the LLMs seem to qualify as authors that originally participate in discursive practices but do display only a structurally derivative form of communicative competence and fail to meet the status of communicative agents. In this sense, while the contribution of AI writing systems in public discourse and deliberation can support the process of mutual understanding within the community of speakers, the human actors involved in the development, use, and diffusion of these systems share a collective responsibility for the disclosure of AI authorship and verification and adjudication of validity claims. (shrink)

This chapter examines the evolving debate on AI legal personhood, emphasizing the role of path dependencies in shaping current trajectories and prospects. Two primary path dependencies emerge: prevailing legal theories on personhood (singularist vs. clustered) and the impact of technological advancements. We argue that these factors dynamically interact, with technological optimism fostering broader rights-based debates and periods of skepticism narrowing discussions to limited rights. Additional influences include regulatory cross-linkages (e.g., data privacy, liability, cybersecurity) and historical legal precedents. Current regulatory frameworks, (...) particularly in the EU, generally resist extending legal personhood to AI systems. Case law suggests that without explicit legislation, courts are unlikely to grant AI legal personhood on their own. For this to happen, AI systems would first need to prove de facto legitimacy through sustained social participation. The chapter concludes by assessing near- and long-term prospects, from generative AI and AI agents in the next 5–20 years to transformative possibilities such as AI integration with human cognition via Brain-Machine Interfaces in a more distant future. (shrink)

Since the release of OpenAI's ChatGPT, the world has entered a race to develop more capable and powerful AI, including artificial general intelligence (AGI). The development is constrained by the dependency of AI on the model, quality, and quantity of training data, making the AI training process highly costly in terms of resources and environmental consequences. Thus, improving the effectiveness and efficiency of the AI training process is essential, especially when the Earth is approaching the climate tipping points and planetary (...) boundaries. It is evident that AI can function better when trained to mimic certain human mental processes. Based on insights from quantum mechanics and the informational entropy-based notion of value, we suggest AI developers can enhance the AI training processes' effectiveness and efficiency by creating and implementing parameter systems that are capable of effectively assigning probabilities to informational quanta. Such systems can help reduce entropy within the system, which lowers the energy required for data storage and processing while decreasing the likelihood of information loss during training. Successfully applying the concept of information entropy-based value in AI development will be crucial for advancing generative AI and achieving AGI in a sustainable manner. (shrink)

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact (...) can give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model BERT and (...) the differences between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis. (shrink)

Sự xuật hiện của các công cụ AI tạo sinh trên nền tảng các mô hình ngôn ngữ lớn (LLMs) đã đem đến một công cụ mới cho con người, đặc biệt là trong các ngành sư phạm, báo chí, nhưng chúng cũng đem đến nhiều vấn đề Trong bài viết này, nhóm tác giả sẽ chỉ ra những những bất cập mới xuất hiện hoặc những vấn đề đã tồn tại nhưng có nguy cơ được đẩy lên cao hơn (...) do sự áp dụng của AI tạo sinh vào trong thực tế, tập trung vào hai nhóm vấn đề: công bằng xã hội và phổ cập tri thức khoa học và công tác truyền thông chính sách. Cụ thể, chúng tôi cho rằng các công cụ AI tạo sinh gặp khó khăn trong việc tiếp cận đến những nguồn kiến thức thuộc các văn hóa thiểu số, hoặc các ngôn ngữ nhỏ, có thể gây khó khăn trong việc tuyên truyền kiến thức khoa học, thực hiện chính sách. Tất cả những khó khăn này sẽ tạo ra những vấn đề tại Việt Nam và cần được giải quyết bằng việc phát triển một bộ giá trị, đạo đức và quy tắc ứng xử khi tương tác và sử dụng AI. (shrink)

Ikigai is a Japanese concept, which, in brief, refers to the “reason or purpose to live”. I-risks have been identified as a category of risks complementing x- risks, i.e., existential risks, and s-risks, i.e., suffering risks, which describes undesirable future scenarios in which humans are deprived of the pursuit of their individual ikigai. While some developments in AI increase i-risks, there are also AI-driven virtual opportunities, which reduce i-risks by increasing the space of potential ikigais, largely due to developments in (...) generative AI, virtual worlds as well as AI-driven hyper-personalization. The purpose of this paper is to present a first attempt to map the potential AI-driven virtual hyper-personalised ikigai universe. Moreover, challenges and further ideas are presented. (shrink)

Generative AI systems like ChatGPT and Midjourney can produce prose or images. But can they produce art? I argue that this question, though natural and intriguing, is the wrong one to ask. A better question is this: can generative AI yield distinct or novel forms of aesthetic value? And I argue that the answer is yes. Generative AI can be used to put us in contact with the artificial sublime – a type of aesthetic value that Kant (...) famously argues is impossible. Kant claims that sublimity (a fusion of trepidation and wonderment) can only arise from encounters with nature, or perhaps close artistic depiction of natural scenes. I show how generative AI can yield encounters both with incalculable profundity (Kant’s mathematical sublime) as well as overwhelming moral chaos (Kant’s dynamical sublime). There are lessons not only for art criticism, but also for the philosophical theory of value. (shrink)

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...) effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

Sora from Open AI has shown exceptional performance, yet it faces scrutiny over whether its technological prowess equates to an authentic comprehension of reality. Critics contend that it lacks a foundational grasp of the world, a deficiency V-JEPA from Meta aims to amend with its joint embedding approach. This debate is vital for steering the future direction of Artificial General Intelligence(AGI). We enrich this debate by developing a theory of productive imagination that generates a coherent world model based on Kantian (...) philosophy. We identify three indispensable components in the coherent world model that enable genuine world understanding: latent representations of isolated objects, an a priori law of change across space and time, and Kantian categories. Our analysis reveals that Sora is limited because of its oversight of the a priori law of change and Kantian categories, flaws that are not rectifiable through scaling up the training. V-JEPA learns the context-dependent aspect of the a priori law of change. Yet it fails to fully comprehend Kantian categories and incorporate experience, leading us to conclude that neither system currently achieves a comprehensive world understanding. Nevertheless, each system has developed components essential to advancing an integrated AI productive imagination-understanding engine. Finally, we propose an innovative training framework for an AI productive imagination-understanding engine, centered around a joint embedding system designed to transform disordered perceptual input into a structured, coherent world model. Our philosophical analysis pinpoints critical challenges within contemporary video AI technologies and a pathway toward achieving an AI system capable of genuine world understanding, such that it can be applied for reasoning and planning in the future. (shrink)

ChatGPT – a large language model – recently passed the U.S. bar exam. The startling rise and power of generative artificial intelligence (AI) systems such as ChatGPT lead us to consider whether and how more specialized systems could be used to overcome existing barriers to the legal system. Such systems could be employed in either of the two major stages of the pursuit of justice: preliminary information gathering and formal engagement with the state’s legal institutions and professionals. We focus (...) on the former and argue that developing and deploying publicly funded AI legal advisors can reduce economic and shame-based cultural barriers to the information-gathering stage of pursuing justice. (shrink)

According to virtue epistemology, the main aim of education is the development of the cognitive character of students (Pritchard, 2014, 2016). Given the proliferation of technological tools such as ChatGPT and other LLMs for solving cognitive tasks, how should educational practices incorporate the use of such tools without undermining the cognitive character of students? Pritchard (2014, 2016) argues that it is possible to properly solve this ‘technology-education tension’ (TET) by combining the virtue epistemology framework with the theory of extended cognition (...) (EXT) (Clark and Chalmers, 1998). He argues that EXT enables us to consider tools as constitutive parts of the students’ cognitive system, thus preserving their cognitive character from technologically induced cognitive diminishment. The first aim of this paper is to show that this solution is not sufficient to solve the TET. Second, I aim to offer a complementary and more encompassing framework of tool-use to address the TET. Then, I apply it to the educational uses of ChatGPT as the most notable example of LLM, although my arguments can be extended to other generative AI systems. To do so, in Sect. 1.1, I present Pritchard’s framework of cognitive character and virtue epistemology applied in education, to which I am committed in this treatment. In Sects. 2 and 3, I respectively illustrate Pritchard’s (2014) solution to the TET, and I highlight the general limitations of his proposal. Thus, in Sect. 4.1 I characterize ChatGPT as a computational cognitive artifact using Fasoli’s (2017, 2018) taxonomy of cognitive artifacts. In Sect. 4.2, I introduce my proposal, which combines Pritchard’s account of virtue epistemology with Fasoli’s (2017, 2018) taxonomy of cognitive artifacts to address the TET. Finally, in Sect. 5.1, I present some epistemically virtuous uses of ChatGPT in educational contexts. To conclude, I argue in favor of a multidisciplinary approach for analyzing educational activities involving AI technologies such as ChatGPT. (shrink)

Artificial intelligence is now capable of generating sophisticated and compelling images from simple text prompts. In this paper, I focus specifically on how artists might make use of AI to create art. Most existing discourse analogizes AI to a tool or collaborator; this focuses our attention on AI’s contribution to the production of an artistically significant output. I propose an alternative approach, the exploration paradigm, which suggests that artists instead relate to AI as a participant: artists create a space for (...) interaction with the AI algorithm by way of their prompts, thereby allowing them to explore the way that the algorithm “sees” and “represents.” AI art practiced in this fashion bears a striking resemblance to contemporary conceptual and participatory art. Viewing AI art in this way has implications for the appreciation of style in AI art; concerns about novelty and originality; and the assignment of artistic credit and copyright. (shrink)

The generative AI boom is unleashing its minions. Enterprise software vendors have rolled out legions of automated assistants that use large language model (LLM) technology, such as ChatGPT, to offer users helpful suggestions or to execute simple tasks. These so-called copilots and chatbots can increase productivity and automate tedious manual work. In this article, we explain how that leads to the risk that users' ethical competence may degrade over time — and what to do about it.

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By enabling (...) rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

Andy Clark and David Chalmers’s theory of extended mind can be reevaluated in today’s world to include computational and Artificial Intelligence (AI) technology. This paper argues that AI can be an extension of human mind, and that if we agree that AI can have mind, it too can be extended. It goes on to explore the example of Ganbreeder, an image-making AI which utilizes human input to direct behavior. Ganbreeder represents one way in which AI extended mind could be achieved. (...) The argument of this paper is that AI can utilize human input as a social extension of mind, allowing AI to access the external world that it would not otherwise be able to access. (shrink)

The rapid advancement of Artificial Intelligence (AI) and the developments of Large Language Models (LLMs) like Generative Pretrained Transformers (GPTs) have significantly influenced content creation in scholarly communication and across various fields. This paper presents a comparative analysis of the content reliability between human-generated and GPT-generated scholarly articles. Recent developments in AI suggest that GPTs have become capable in generating content that can mimic human language to a greater extent. This highlights and raises questions about the quality, accuracy, and (...) reliability of such content, especially in academic contexts. Statistical evaluations and quantitative assessments conducted in this study uncover key differences in content accuracy, coherence, citation usage, and overall reliability between human and GPT-generated articles. The paper also examines the potential biases, the role of context, and the implications of AI-generated content for the future of scholarly communication. The study concludes with framework for predictive model indicating the potential future impact of GPT-driven content creation and recommendations for ensuring content quality and ethical considerations in AI usage. (shrink)

Ask most any cognitive scientist working today if a digital computational system could develop aesthetic sensibility and you will likely receive the optimistic reply that this remains an open empirical question. However, I attempt to show, while drawing upon the later Wittgenstein, that the correct answer is in fact available. And it is a negative a priori. It would seem, for example, that recent computational successes in generative AI and textual attribution, most notably those of Donald Foster (famed finder (...) of Ted Kazinski a.k.a. “the Unibomber”) speak favorably of the connectionist model's capacity to overcome the “aspect blindness” handicap in this domain. I argue however that such results are only achievable when rigid input‐to‐output parameters are given, and that this element is precisely what is absent in standard examples of aesthetic judgment. I thus conclude that while the connectionist model anticipated by Turing may provide the best approach for the AI project, its capacity for meeting its own sufficiency requirements is necessarily crippled by its inability to share in what can be generally referred to as the collective engagements of human solidarity. (shrink)

The value of testimony as a source of knowledge has been a subject of epistemological debates. The "trust theory of testimony" suggests that human testimony is based on an affective relationship supported by social norms. However, the advent of generative artificial intelligence challenges our understanding of genuine testimony. The concept of "quasi-testimony" seeks to characterize utterances produced by non-human entities that mimic testimony but lack certain fundamental attributes. This article analyzes these issues in depth, exploring philosophical perspectives on testimony (...) and the implications of conversational AI technologies on our epistemic practices. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered by conflations (...) of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (shrink)

Psychoanalysis, particularly as articulated by figures like Freud and Lacan, highlights the inherent division within the human subject—a schism between the conscious and unconscious mind. It could be said that this suggests that such an internal division becomes amplified in the context of generative art, where technology and algorithms are used to generate artistic expressions that are meant to emerge from the depths of the unconscious. Here, we encounter the tension between the conscious artist and the generative process (...) itself, which may yield unexpected, even uncontrollable results. -/- This paper, therefore, seeks to address this division within the modern subject and its relationship to technology, wherein the division within the living body is revealed through the presence of prosthetic elements, which mirrors the division brought about by the incorporation of language as a signifier. I argue that the amplification of this internal schism does not necessarily lead to a more fractured subject. Instead, generative art, bolstered by advancements in AI and machine learning, offers a unique opportunity for individuals to externalize and explore their minds in novel ways. -/- By examining contemporary works such as Hal Foster’s Prosthetic Gods, which stands as a pivotal exploration of the convergence between modernist art and psychoanalytic theory and Isabel Millar’s Psychoanalysis of Artificial Intelligence, this paper elucidates the profound implications of Freud’s vision of modern -/- subjectivity as Prothesengott (Prosthetic God) and address the questions concerning this technological imbrication of the human mind and body through the Lacanian framework. Although for Freud, Man does not become a real God, rather, the potential to transcend one’s limitations ascribes us to God-like qualities by seeking to generate new forms of life that go beyond merely reproducing nature — a transcention of the natural. Millar emphasizes that Freud observes that this is evidenced by the fact that these additional organs remain distinct from the organism and can never assimilate into it. One continually falls short of realizing the fantasy he envisions, opting instead to use his supplementary artificial organs to endlessly revolve around the objects of the drive. -/- This evolving relationship that the drive has with its technological objects, resounds in Lacan’s conception of “lathouse” which allows extimate objects to convert interiority (unconscious) into exteriority (conscious) and exteriority into interiority. The thesis of this paper seeks to employ this underutilized concept to understand the nature of human subjectivity and its bodily and structural relationship to generative art. Therefore, this paper emphasizes what really happens when we enter into this relationship with the lathouse, whereby this artificial object has effects in the "real of jouissance", where these Lathouses create a network, namely the Alethosphere. My goal is to argue that generative art as a technological development, can be seen as an extension to the development of the drive. Conclusively, I make the claim for generative art's potential to externalize the human creative drive by emphasizing the interplay between randomness and structure, and how it offers a means to surpass our inherent limitations by presenting an avenue for self-expression that transcends traditional modes of art. (shrink)

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well as faculty (...) and staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future. (shrink)

This chapter considers the potential of artificial intelligence (AI) to exhibit creativity and imagination, in light of recent advances in generative AI and the use of deep neural networks (DNNs). Reasons for doubting that AI exhibits genuine creativity or imagination are considered, including the claim that the creativity of an algorithm lies in its developer, that generative AI merely reproduces patterns in its training data, and that AI is lacking in a necessary feature for creativity or imagination, such (...) as consciousness, agency, metacognitive awareness, or the use of mental imagery. In each case, a counterargument is provided that AI already has or soon will overcome the apparent barrier. In some instances, this is because the critical features deemed lacking in AI may not be necessary for human creativity and imagination either. In others, it is because important developmental and structural similarities between humans and generative AI have been insufficiently appreciated. (shrink)

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead (...) to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology. (shrink)

This study aims to understand the perceptions and opinions of academicians towards ChatGPT-3 by collecting and analyzing social media comments, and a survey was conducted with library and information science professionals. The research uses a content analysis method and finds that while ChatGPT-3 can be a valuable tool for research and writing, it is not 100% accurate and should be cross-checked. The study also finds that while some academicians may not accept ChatGPT-3, most are starting to accept it. The study (...) is beneficial for academicians, content developers, and librarians. (shrink)

Human civilization is very clearly reaching a point of critical mass when it comes to technology and how it transforms culture and the economics that is therefore driven forward. The conversation around the practical aspects of generative artificial intelligence (Chat GPT, Q Star, Bard, Claude, Genesis, Firefly, and others) and their ethical implications is massively trending. The political conversations around it are slow to catch up but will soon take over once the general public feels their impact, which is (...) likely to happen in the next two to three years. -/- What is not being spoken of is the need for new economic models that can integrate the enormous and continuous shifts in trade that will occur, as we continue to implement more efficient manufacturing and logistics. Things like 3D printing in hub regions, the automation of transportation, augmented and virtual reality, and new creativity-based industries will all disrupt capital markets as well as social systems due to what we could call "meta-scarcity", which is to say, "the scarcity of scarcity"; In effect, we will lack the scarcity to maintain predictive power over the future. This is because when technology is capable of optimizing for creativity, people come up with new demands, and when new demands are supplied as quickly as they arise, no one is capable of knowing what kind of trends the market will take, yet it is necessary to try as public discussion could beneficially shape the economic landscape. (shrink)

The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, (...) whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied. (shrink)

Extended Reality (XR) and Large Language Model (LLM) technologies have the potential to significantly influence higher education practices and pedagogy in the coming years. As these emerging technologies reshape the educational landscape, it is crucial for educators and higher education professionals to understand their implications and make informed policy decisions for both individual courses and universities as a whole. -/- This paper has two parts. In the first half, we give an overview of XR technologies and their potential future role (...) in higher education. Then, we discuss the benefits and challenges of adopting and integrating XR technologies into higher education settings. Finally, we offer recommendations for educators, administrators, and policymakers on how to approach the adoption of this set of technologies and set institutional policy to take advantage of its benefits while minimizing its potential negative consequences. -/- In the paper’s second half, we discuss LLMs - specifically Generative Pre-trained Transformers (GPTs). Again, we provide an overview of this rapidly developing technology and examine its unprecedented and potentially disruptive impact on higher education pedagogy. We delve into the benefits and limitations of GPTs, highlighting the pedagogical and ethical challenges they may pose. Finally, we present actionable recommendations for instructors and administrators at both the course level and the university-wide level to guide policy decisions concerning these transformative technologies. (shrink)

This study explores the integration of ChatGPT as a teaching assistant in physics education, emphasizing its potential to transform traditional pedagogical approaches. ChatGPT facilitates interactive and inquiry-based learning grounded in constructivist learning theory, allowing students to engage actively in experiments and better grasp abstract concepts through hands-on activities. The AI's adaptive dialogue systems promote socio-constructivist learning by encouraging social interaction and personalized feedback, which is essential for addressing individual learning gaps and enhancing student engagement. ChatGPT's ability to simulate real-world physics (...) problems and provide immediate feedback fosters experiential learning, making complex concepts more accessible and promoting critical thinking skills. By offering tailored interventions and adapting to individual learning paces, ChatGPT supports a personalized educational experience that caters to the unique needs of each student. This adaptability is particularly beneficial in physics education, where students often struggle with abstract concepts and require immediate clarification to progress effectively. The paper concludes that the synthesis of generative AI and pedagogy has the potential to reshape science education, fostering deeper understanding and curiosity among students. By leveraging innovative methodologies and augmented data, ChatGPT enriches teacher-student interactions, creating a comprehensive educational experience that promotes a culture of curiosity and exploration, thereby nurturing future scientists and engineers. Ultimately, the integration of ChatGPT into physics education offers a valuable opportunity to enhance student engagement and understanding of scientific concepts through interactive and personalized support. (shrink)

With the recent renewed interest in AI, the field has made substantial advancements, particularly in generative systems. Increased computational power and the availability of very large datasets has enabled systems such as ChatGPT to effectively replicate aspects of human social interactions, such as verbal communication, thus bringing about profound changes in society. In this paper, we explain that the arrival of generative AI systems marks a shift from ‘interacting through’ to ‘interacting with’ technologies and calls for a reconceptualization (...) of socio-technical systems as we currently understand them. We dub this new generation of socio-technical systems synthetic to signal the increased interactions between human and artificial agents, and, in the footsteps of philosophers of information, we cash out agency in terms of ‘poiêsis’. We close the paper with a discussion of the potential policy implications of synthetic socio-technical system. -/- . (shrink)

There is an underappreciated respect in which the widespread availability of generative artificial intelligence (AI) models poses a threat to human connection. My central contention is that human creativity is especially capable of helping us connect to others in a valuable way, but the widespread availability of generative AI models reduces our incentives to engage in various sorts of creative work in the arts and sciences. I argue that creative endeavors must be motivated by curiosity, and so they (...) must disclose the creative agent’s inquisitive self. It is through self-disclosure, including the disclosure of the inquisitive self, that we put ourselves in a position to be seen by and connect with others through our creative pursuits. Because relying on AI for certain generative tasks is less self-disclosive than the creative work such technologies supplant, this reliance threatens to weaken our connections to one another. (shrink)

Jonathan takes us through his experiences of being a mentor for UTOP (University Teaching Opportunities Programme), particularly how it enabled him to collaborate with his UTOP student mentees to design a learning activity in which students could think critically about AI-generated output.